INFORMAZIONI SU QUESTO EPISODIO
This episode features an interview with Nikita Shamgunov, legendary founder of MemSQL (now SingleStore). His latest endeavor, Neon, offers serverless Postgres as a fully managed multi-cloud database that separates storage and compute, with auto scaling, branching, and bottomless storage.
Nikita is also a Partner at Khosla Ventures, where he is incubating Neon and raised $104M to date. He is passionate about deep tech, data infrastructure, and system software. Prior to Neon, Nikita co-founded MemSQL (now SingleStore), a unicorn data and analytics company valued at over $1.3 billion. He served as a founding CTO, and then CEO, successfully scaling the company to over $40 million in ARR. Prior to SingleStore, he worked as a senior engineer at Facebook and at Microsoft on SQL Server. Nikita earned a Ph.D. in computer science from the National Research University in St. Petersburg, Russia.
In this episode, Nikita recounts the founding stories behind both MemSQL and Neon, and elaborates on the key trends driving database technologies today, from serverless and generative AI, to open data and the convergence of transactional and analytical workloads.
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Key Quotes
Amplitude and Mixpanel, they basically are a time series database underneath with the UI. Time series data tends to be, you know, ‘write once’, most of it. And so, you need to take advantage of those techniques that data warehouses are basically born with, right? They are in the business of storing data relatively cheaply. And every enterprise, unless it's not like an archaic enterprise, should have a data warehouse. So it makes only too much sense to put this into a data warehouse rather than either a custom database, you know, like a platform like Datadog, Mixpanel, Amplitude. Plus you have additional benefits from it because you can cross reference that data with the rest of the business data." - Nikita Shamgunov
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Episode Timestamps
(01:41) Founding stories behind MemSQL and Neon
(03:39) Addressing new challenges for databases
(09:20) Criteria for evaluating databases
(12:36) HTAP and zero ETL between transactional and analytical applications
(19:07) Evolving standards around table formats
(24:07) Thoughts on Generative AI and LLM-native in the data warehouse
(26:38) Warehouse centric approaches to data storage
(29:45) Open source for data warehouses
(33:54) Potential for new applications to be built around real time applications
(38:10) Managing large volumes of data
(40:59) Serverless Postgres is as easy as Stripe
(45:40) Takeaways
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Links
